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David Charte
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COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images
S Tabik, A Gómez-Ríos, JL Martín-Rodríguez, I Sevillano-García, ...
IEEE journal of biomedical and health informatics 24 (12), 3595-3605, 2020
3612020
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
D Charte, F Charte, S García, MJ del Jesus, F Herrera
Information Fusion 44 (November 2018), 78-96, 2017
3212017
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
JM Górriz, J Ramírez, A Ortíz, FJ Martinez-Murcia, F Segovia, J Suckling, ...
Neurocomputing 410, 237-270, 2020
1832020
Working with Multilabel Datasets in R: The mldr Package
F Charte, FD Charte
The R Journal 7 (2), 149--162, 2015
772015
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges
D Charte, F Charte, MJ del Jesus, F Herrera
Neurocomputing 404, 93-107, 2020
662020
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations
D Charte, F Charte, S García, F Herrera
Progress in Artificial Intelligence 8 (1), 1-14, 2018
362018
Tips, guidelines and tools for managing multi-label datasets: The mldr. datasets R package and the Cometa data repository
F Charte, AJ Rivera, D Charte, MJ del Jesus, F Herrera
Neurocomputing 289, 68-85, 2018
312018
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges
J Luengo, R Moreno, I Sevillano, D Charte, A Pelaez-Vegas, ...
Information Fusion 78, 232-253, 2022
292022
R ultimate multilabel dataset repository
F Charte, D Charte, A Rivera, MJ del Jesus, F Herrera
Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS …, 2016
262016
Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect
JD Pascual-Triana, D Charte, MA Arroyo, A Fernández, F Herrera
Knowledge and Information Systems, 2021
172021
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
D Charte, F Charte, F Herrera
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
172021
Ruta: Implementations of neural autoencoders in R
D Charte, F Herrera, F Charte
Knowledge-Based Systems 174, 4 - 8, 2019
92019
A showcase of the use of autoencoders in feature learning applications
D Charte, F Charte, MJ del Jesus, F Herrera
International Work-Conference on the Interplay Between Natural and …, 2019
52019
Slicer: feature learning for class separability with least-squares support vector machine loss and COVID-19 chest X-ray case study
D Charte, I Sevillano-García, MJ Lucena-González, JL Martín-Rodríguez, ...
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS …, 2021
32021
How to work with multilabel datasets in R using the mldr package
F Charte, FD Charte
Figshare, 2015
22015
mldr: Paquete R para Exploración de Datos Multietiqueta
D Charte, F Charte
Proc. 16th Conferencia de la Asociación Española Para la Inteligencia …, 2015
12015
Machine Learning y Ciencia de Datos con Python y R
F Charte, D Charte
Krasis Press - 978-84-945822-5-7, 2021
2021
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